用几何模式与分类模型辅助识别皮肤癌的不对称病灶，提升非专家诊断准确率。
Asymmetric Lesion Detection with Geometric Patterns and CNN-SVM Classification
- 通过几何模式分析病灶不对称性，辅助非专家理解诊断标准。
- 几何方法检测率达99.00%，卷积网络结合SVM分类器在多类任务中表现优异。
- 适合医学影像辅助诊断、皮肤科筛查场景，尤其帮助临床新手判断病变形态。
在皮肤镜图像中，病灶形状为皮肤疾病诊断提供关键信息。临床实践中，不对称形状是黑色素瘤的重要判别依据。我们首先基于临床评估对一个未标注数据集进行对称性标注。随后提出一种监督学习图像处理算法，用于分析病灶形状的几何模式，帮助非专家理解不对称病灶的判断标准。接着利用预训练卷积神经网络（CNN）提取皮肤镜图像中的形状、颜色和纹理特征，训练多分类支持向量机（SVM）分类器，性能优于现有文献中的先进方法。几何分析实验中，不对称病灶检测率达到99.00%；在基于CNN的实验中，最佳结果为94%的Kappa评分、95%的宏平均F1分数和97%的加权平均F1分数，用于区分不对称、半对称与对称三类病灶。
原文摘要 · Abstract (English)
In dermoscopic images, which allow visualization of surface skin structures not visible to the naked eye, lesion shape offers vital insights into skin diseases. In clinically practiced methods, asymmetric lesion shape is one of the criteria for diagnosing melanoma. Initially, we labeled data for a non-annotated dataset with symmetrical information based on clinical assessments. Subsequently, we propose a supporting technique, a supervised learning image processing algorithm, to analyze the geometrical pattern of lesion shape, aiding non-experts in understanding the criteria of an asymmetric lesion. We then utilize a pre-trained convolutional neural network (CNN) to extract shape, color, and texture features from dermoscopic images for training a multiclass support vector machine (SVM) classifier, outperforming state-of-the-art methods from the literature. In the geometry-based experiment, we achieved a 99.00% detection rate for dermatological asymmetric lesions. In the CNN-based experiment, the best performance is found with 94% Kappa Score, 95% Macro F1-score, and 97% Weighted F1-score for classifying lesion shapes (Asymmetric, Half-Symmetric, and Symmetric).
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